About this Dataset

S&P 500 Companies Price Dynamics

This data set includes the S&P500 price dynamics data generated using an Open Source project yfinance (https://pypi.org/project/yfinance/). The dates interval starts from 2018 up to the beginning date of the current month at the daily granularity.

Questions you might be interested in answering:

Calculate monthly returns for each stock
What are the most profitable sectors and industries each year?
What sectors and specific stocks have outperformed the S&P 500 index in each year?
Calculate the yearly Sharpe ratio: [Return - Risk-Free Rate] / StDev([Return - Risk-Free Rate]). For the Risk-Free rate you might want to use UST 10Y rates
What stocks and sectors have the best risk-adjusted returns (shape ratio) in each year?
What might be the reasons for observed trends? What trends should we expect for 2023?
What trading strategy would you choose regarding the S&P 500 stocks?

Tables

Yearly Returns Stocks 2018–2023

@kaggle.alexanderkuznetsovow_s_and_p_500_companies_price_dynamics.yearly_returns_stocks_2018_2023

121.2 KB
3006 rows
10 columns


CREATE TABLE yearly_returns_stocks_2018_2023 (
  "company" VARCHAR,
  "year" BIGINT,
  "yearly_return" DOUBLE,
  "risk_free_r" DOUBLE,
  "excess_return" DOUBLE,
  "stdev_return" DOUBLE,
  "sector" VARCHAR,
  "industry" VARCHAR,
  "stdev_excess_return" DOUBLE,
  "sharpe_ratio" DOUBLE
);

Yearly Returns Index 2018–2023

@kaggle.alexanderkuznetsovow_s_and_p_500_companies_price_dynamics.yearly_returns_index_2018_2023

7.57 KB
6 rows
10 columns


CREATE TABLE yearly_returns_index_2018_2023 (
  "company" VARCHAR,
  "year" BIGINT,
  "yearly_return" DOUBLE,
  "risk_free_r" DOUBLE,
  "excess_return" DOUBLE,
  "stdev_return" DOUBLE,
  "sector" VARCHAR,
  "industry" VARCHAR,
  "stdev_excess_return" DOUBLE,
  "sharpe_ratio" DOUBLE
);

Clean Sp500 Index 2018–2023

@kaggle.alexanderkuznetsovow_s_and_p_500_companies_price_dynamics.clean_sp500_index_2018_2023

12.64 KB
65 rows
14 columns


CREATE TABLE clean_sp500_index_2018_2023 (
  "company" VARCHAR,
  "open" DOUBLE,
  "high" DOUBLE,
  "low" DOUBLE,
  "close" DOUBLE,
  "volume" BIGINT,
  "dividends" DOUBLE,
  "stock_splits" DOUBLE,
  "year" BIGINT,
  "month" BIGINT,
  "day" BIGINT,
  "mo_return" DOUBLE,
  "gics_sector" VARCHAR,
  "gics_sub_industry" VARCHAR
);

Ust 10–2018–2023

@kaggle.alexanderkuznetsovow_s_and_p_500_companies_price_dynamics.ust_10_2018_2023

9.65 KB
65 rows
11 columns


CREATE TABLE ust_10_2018_2023 (
  "company" VARCHAR,
  "open" DOUBLE,
  "high" DOUBLE,
  "low" DOUBLE,
  "close" DOUBLE,
  "volume" BIGINT,
  "dividends" DOUBLE,
  "stock_splits" DOUBLE,
  "year" BIGINT,
  "month" BIGINT,
  "day" BIGINT
);

Clean Sp500 Stock 2018–2023

@kaggle.alexanderkuznetsovow_s_and_p_500_companies_price_dynamics.clean_sp500_stock_2018_2023

2.02 MB
32282 rows
19 columns


CREATE TABLE clean_sp500_stock_2018_2023 (
  "company" VARCHAR,
  "open" DOUBLE,
  "high" DOUBLE,
  "low" DOUBLE,
  "close" DOUBLE,
  "volume" DOUBLE,
  "dividends" DOUBLE,
  "stock_splits" DOUBLE,
  "year" BIGINT,
  "month" BIGINT,
  "day" BIGINT,
  "security" VARCHAR,
  "gics_sector" VARCHAR,
  "gics_sub_industry" VARCHAR,
  "founded" VARCHAR,
  "mo_return" DOUBLE,
  "perform_vs_sp" DOUBLE,
  "perform_flag" BIGINT,
  "time_higher_sp" DOUBLE
);